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smerc (version 1.3)

Statistical Methods for Regional Counts

Description

Implements statistical methods for analyzing the counts of areal data, with a focus on the detection of spatial clusters and clustering. The package has a heavy emphasis on spatial scan methods, which were first introduced by Kulldorff and Nagarwalla (1995) and Kulldorff (1997) .

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Version

Install

install.packages('smerc')

Monthly Downloads

849

Version

1.3

License

GPL (>= 2)

Maintainer

Joshua French

Last Published

October 7th, 2020

Functions in smerc (1.3)

arg_check_dist_ellipse

Check argments if dist.ellipse

Determine case windows (circles)

Cluster Evalation Permutation Procedure Test

Besag-Newell Test

Compute region weights for cepp.test

Perform cepp.test on simulated data

Combine distinct zones

Construct connected subgraphs

all_shape_dists

Return all shapes and distances for each zone

Dynamic Minimum Spanning Tree spatial scan test

Perform edmst.test on simulated data

Determine zones for elliptic.test

Perform fast.test on simulated data

Determine zones for the maxima likelihood first algorithm.

Perform mlink.test on simulated data

Apply getElement over a list

Convert logical vector to zone

Nearest neighbors for elliptic scan

Determine zones for the early stopping dynamic Minimum Spanning Tree scan test

Determine zones for the Dynamic Minimum Spanning Tree scan test

Perform dc.test on simulated data

Distinct elements of a list

Perform dmst.test on simulated data

Spatial scan statistic

Determine zones for the Double Connected scan test

Cumulative sum over nearest neighbors

Determine sequence of fast subset scan zones

Fast Subset Scan Test

Determine zones for flexibly shaped spatial scan test

Determine nearest neighbors based on maximum distance

K nearest neighbors

Determine zones for the spatial scan test

Print object of class tango.

Compute middle p-value

Convert nearest neighbors list to zones

Compute minor axis distance of ellipse

Spatial scan statistic

Returns segments connecting neighbors

Perform flex.test on simualated data

Determine zones for flexibly shaped spatial scan test

Double Connection spatial scan test

Flexibly-shaped Spatial Scan Test

Minimum spanning tree sequence

Minimum spanning tree for all regions

SpatialPolygonsDataFrame for New York leukemia data.

Flexibly-shaped Spatial Scan Test

Adjacency matrix for New York leukemia data.

Early Stopping Dynamic Minimum Spanning Tree spatial scan test

Perform elliptic.test on simulated data

Return most significant, non-overlapping zones

Maxima Likelihood First Scan Test

Leukemia data for 281 regions in New York.

Compute Monte Carlo p-value

Determine non-overlapping zones

Elliptical Spatial Scan Test

Maximum Linkage spatial scan test

Determine duplicates in nearest neighbor list

Determine zones for the Maximum Linkage scan test

plot.smerc_cluster

Plot object of class smerc_cluster.

Return nicely formatted results from mst.all

print.smerc_cluster

Print object of class smerc_cluster.

Tango's statistic

Plots an object of class tango.

Tango's clustering detection test

Upper Level Set Spatial Scan Test

Spatial Scan Test

Determine sequence of ULS zones.

Perform scan.test on simulated data

Determine nearest neighbors with population constraint

Perform rflex.test on simualated data

Prepare smerc_cluster

Restricted Flexibly-shaped Spatial Scan Test

Determine zones for flexibly shaped spatial scan test

summary.smerc_cluster

Summary of smerc_cluster object

Determine zones for flexibly shaped spatial scan test

Perform uls.test on simulated data

Distance-based weights for tango.test